Can I use one-hot vectors for text classification?
One hot encoding is a good strategy to apply with categorical variables that assume few possible values. The problem with text data is that you easily end up with corpora with a really large vocabulary. If I remember correctly the IMDb dataset contains around 130.000 unique words, which means that you should create a network with an input matrix of size 130.000 x max_length where max_length is the fixed maximum length allowed for each review. Apart from the huge size, this matrix would also be extremely sparse, and that's another big issue in using one-hot encoding with text.
For these reasons, I really doubt you would achieve any good results with a simple one-hot encoding. Embeddings where actually designed precisely to overcome all these issues, they have fixed reasonable size, they assume continue values between 0 and 1, which is desirable for deep neural networks, and they can be treated as &quot;extra&quot; trainable weights of a network.
If you really want to avoid embeddings I would suggest you to use (or implement, I don't think it will be so hard) a term frequency–inverse document frequency vectoriser. It is closer to one-hot encoding in the fact that it is based on the creation of a huge co-occurances matrix between words, but at least the values are continuous and not dichotomous. Nevertheless I would not expect high performances with the tf-idf either, simply because this type of encoding works best with shallow models like the Naive Bayes rather than deep models.